PaDE: An enhanced Differential Evolution algorithm with novel control parameter adaptation schemes for numerical optimization

Mathematical Problems in Engineering

et al. 2020

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Sine Cosine Algorithm (SCA) has been proved to be superior to some existing traditional optimization algorithms owing to its unique optimization principle. However, there are still disadvantages such as low solution accuracy and poor global search ability. Aiming at the shortcomings of the sine cosine algorithm, a multigroup multistrategy SCA algorithm (MMSCA) is proposed in this paper. e algorithm executes multiple populations in parallel, and each population executes a different optimization strategy. Information is exchanged among populations through intergenerational communication. Using 19 different types of test functions, the optimization performance of the algorithm is tested. Numerical experimental results show that the performance of the MMSCA algorithm is better than that of the original SCA algorithm, and it also has some advantages over other intelligent algorithms. At last, it is applied to solving the capacitated vehicle routing problem (CVRP) in transportation. e algorithm can get better results, and the practicability and feasibility of the algorithm are also proved. [29,30], bat algorithm (BA) [31,32], symbiotic organism search algorithm (SOS) [33,34], and QUATRE [35][36][37].At present, there are so many optimization algorithms, which can solve some complex optimization problems well. Why do we need so many optimization algorithms? As demonstrated by the No Free Lunch (NFL) [38] proposed by Wolpert and Macready, no single optimization algorithm is applicable to all problems. Inspired by this, a new intelligent optimization algorithm was proposed by Australian scholar Mirjalili in 2016, which is called Sine Cosine Algorithm (SCA) [39]. e SCA algorithm iterates through the properties of the sine and cosine functions to achieve optimization. It has fewer parameter settings, is easy to implement, and has a strong optimization ability. It has been proved that it is better than PSO algorithm, genetic algorithm (GA), and firefly algorithm (FA) in convergence with accuracy and speed [39].With the rapid development of software and hardware, parallel computing has become a form of high-performance computing. In evolutionary computing, parallelism often represents the iterative updating of multiple populations at the same time. e advantage of this method is to ensure population diversity, to further improve the search ability and performance of the algorithm. Especially when solving complex optimization problems, parallelizing the algorithm is an effective way to improve the efficiency and accuracy of the algorithm. At present, many existing algorithms have successfully applied the parallel mechanism, such as parallel PSO [40], parallel ACO [41], and parallel QUATRE [42]. Inspired by this, this paper introduces the multigroup and multistrategy optimization mechanism to further improve the SCA algorithm. It is called MMSCA. When the algorithm is solved, multiple populations execute in parallel, and each population adopts different updating strategies. By comparing the results of test functions, MMSCA is be...

Section: Resultsmentioning

confidence: 63%

Section: Introductionmentioning

confidence: 99%

Sine Cosine Algorithm with Multigroup and Multistrategy for Solving CVRP

Yang

Mathematical Problems in Engineering

et al. 2020

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“…In future work, the proposed scheme may be further improved by adopting some efficient approaches [47][48][49] for optimal classification parameters; and it also may hybridize with the method of the neural network [50].…”

Section: Resultsmentioning

confidence: 99%

Identifying correctness data scheme for aggregating data in cluster heads of wireless sensor network based on naive Bayes classification

Dao

et al. 2020

J Wireless Com Network

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Wireless sensor network (WSN) has been paid more attention by scholars due to the practical communication of a system of devices to transfer information gathered from a monitored field through wireless links. Precise and accurate data of aggregating messages from sensor nodes is a vital demand for a success WSN application. This paper proposes a new scheme of identifying the correctness data scheme for aggregating data in cluster heads in hierarchical WSN based on naive Bayes classification. The collecting environmental information includes temperature, humidity, sound, and pollution levels, from sensor nodes to cluster heads that classify data fault and aggregate and transfer them to the base station. The collecting data is classified based on the classifier to aggregate in the cluster head of WSN. Compared with some existing methods, the proposed method offers an effective way of forwarding the correct data in WSN applications.

“…In the past several decades, intelligent computing has developed rapidly. Researchers have evolved a variety of intelligent computing algorithms inspired by natural phenomena [1,2]. While conducting research, we often encounter complex problems that require the extreme value of a multivariate function to be found.…”

Section: Introductionmentioning

confidence: 99%

Symbiotic Organism Search Algorithm with Multi-Group Quantum-Behavior Communication Scheme Applied in Wireless Sensor Networks

2020

Applied Sciences

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The symbiotic organism search (SOS) algorithm is a promising meta-heuristic evolutionary algorithm. Its excellent quality of global optimization solution has aroused the interest of many researchers. In this work, we not only applied the strategy of multi-group communication and quantum behavior to the SOS algorithm, but also formed a novel global optimization algorithm called the MQSOS algorithm. It has speed and convergence ability and plays a good role in solving practical problems with multiple arguments. We also compared MQSOS with other intelligent algorithms under the CEC2013 large-scale optimization test suite, such as particle swarm optimization (PSO), parallel PSO (PPSO), adaptive PSO (APSO), QUasi-Affine TRansformation Evolutionary (QUATRE), and oppositional SOS (OSOS). The experimental results show that MQSOS algorithm had better performance than the other intelligent algorithms. In addition, we combined and optimized the DV-hop algorithm for node localization in wireless sensor networks, and also improved the DV-hop localization algorithm to achieve higher localization accuracy than some existing algorithms.